System and method for generating personal vocabulary from network data

ABSTRACT

A method is provided in one example and includes receiving data propagating in a network environment, and identifying selected words within the data based on a whitelist. The whitelist includes a plurality of designated words to be tagged. The method further includes assigning a weight to the selected words based on at least one characteristic associated with the data, and associating the selected words to an individual. A resultant composite is generated for the selected words that are tagged. In more specific embodiments, the resultant composite is partitioned amongst a plurality of individuals associated with the data propagating in the network environment. A social graph can be generated that identifies a relationship between a selected individual and the plurality of individuals based on a plurality of words exchanged between the selected individual and the plurality of individuals.

TECHNICAL FIELD

This disclosure relates in general to the field of communications and, more particularly, to generating personal vocabulary from network data.

BACKGROUND

The field of communications has become increasingly important in today's society. In particular, the ability to effectively gather, associate, and organize information presents a significant obstacle for component manufacturers, system designers, and network operators. This obstacle is made even more difficult due to privacy issues, which seem ubiquitous in today's corporate environments. As new communication platforms and technologies become available, new protocols should be developed in order to optimize the use of these emerging protocols. Some issues have arisen in data monitoring scenarios in which content (sought to be intelligently organized) propagates in the network.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure and features and advantages thereof, reference is made to the following description, taken in conjunction with the accompanying figures, wherein like reference numerals represent like parts, in which:

FIG. 1 is a simplified block diagram of a communication system for generating personal vocabulary from network data in accordance with one embodiment;

FIG. 2 is a simplified block diagram of a central engine in the communication system in accordance with one embodiment;

FIG. 3 is a simplified flowchart illustrating a series of example steps associated with the communication system; and

FIG. 4 is a simplified flowchart illustrating another series of example steps associated with the communication system.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

A method is provided in one example and includes receiving data propagating in a network environment, and identifying selected words within the data based on a whitelist. The whitelist includes a plurality of designated words to be tagged. The method further includes assigning a weight to the selected words based on at least one characteristic associated with the data, and associating the selected words to an individual. A resultant composite is generated for the selected words that are tagged. In more specific embodiments, the resultant composite is partitioned amongst a plurality of individuals associated with the data propagating in the network environment. A social graph can be generated that identifies a relationship between a selected individual and the plurality of individuals based on a plurality of words exchanged between the selected individual and the plurality of individuals. In still other embodiments, the method can include determining whether a threshold weight value associated with the selected words has been met. If the threshold has been met, then the selected words are included in the resultant composite. A different weight value can be assigned to the selected words based on the selected words being sent or received by the individual.

Example Embodiments

FIG. 1 is a simplified block diagram of a communication system 10 for generating personal vocabulary from network data. FIG. 1 may include an end user 12, who is operating a computer device that is configured to interface with an Internet Protocol (IP) network 14. In addition, an administrator 20 is provided, where administrator 20 has the ability to interface with the architecture through an IP network 18. Communication system 10 may further include a network collaboration platform (NCP) 32, which includes an add to whitelist/blacklist module 34, a feedback loop module 36, and an administrator suggest interface 38. FIG. 1 may also include a central engine 40, which includes a lightweight directory access protocol (LDAP) feeder element 42, a vocabulary feeder module 44, an emerging vocabulary topics element 46, and a table write service element 48.

FIG. 1 may also include a network sensor 54 that includes a first in, first out (FIFO) element 56, a text extraction module 58, a blacklist 60, a document type filter 62, a noun phrase extractor module 64, a whitelist 66, a document splitter element 68, and a clean topics module 70. Multiple network sensors 54 may be provisioned at various places within the network and such provisioning may be based on how much information is sought to be tagged, the capacity of various network elements, etc.

Note that before turning to the example flows and infrastructure of example embodiments of the present disclosure, a brief overview of a workflow of the architecture is provided. Certain automatic vocabulary generation protocols attempt to solve the problem of manually building a vocabulary. Most approaches are time consuming, inflexible, and difficult to manage. Communication system 10 can offer an architecture that provides an effective tagging for content propagating through a given network. By having a corpus of words, communication system 10 can provide a number of important features to an administrator. For example, by intelligently harvesting network data from a variety of end users, the architecture can automatically create personal vocabulary from business vocabulary by observing each user's interaction/traffic on the network. In a general sense, the architecture can isolate terms per person in order to define an end user's personal vocabulary.

This information can subsequently be used to identify specific experts. In other instances, the personal vocabulary can be used for topic-based social graph building (e.g., social networking applications). In other instances, this information can be used to improve the accuracy of speech-to-text translations, which can relate to the individual applications being used by the person, a particular environment in which the end user participates, feature invocation applications, etc. The solution can intelligently and dynamically auto generate different lists of personal vocabulary per user without creating additional overhead for the end users.

Note that current architectures fail to offer vocabulary building activities, which can represent each individual person, their context, or their social graph (person to person, a group of persons over different forms of communication, etc.). The frequency in which words are used per individual is not tracked, nor is the intersection or context in which words are used. Furthermore, the popularity of words being employed by an end user (for example, over a specific time interval) is not evaluated.

In contrast to these limited operations, communication system 10 can tag words for specific end users. For example, relevant words identified in an enterprise system can be extracted from the documents, which are flowing through the network. The tags can be categorized and then associated to the user, who generated or who consumed each document. In accordance with one example implementation, a tag can be given different weights depending on several possible document characteristics.

One characteristic relates to the type of document propagating in the network (for example, email, an HTTP transaction, a PDF, a Word document, a text message, an instant message, etc.). Another characteristic relates to the type of usage being exhibited by the end user. For example, the system can evaluate if the end user represents the producer of the content (e.g., the sender, the poster, etc.), or the consumer of the content (e.g., the recipient, the audience member, etc.). In one example, if the end user is posting a document including the identified vocabulary, the act of posting such words would accord the words a higher weight, than merely receiving an email that includes the particular vocabulary words. Stated in different terms, in a forum in which the end user is authoring a document to be posted (e.g., on a blog, on a corporate website, in a corporate engineering forum, etc.), vocabulary words within that document would have a higher associative value than if the words were propagating in lesser forums (e.g., a passive recipient in an email forum). Yet another characteristic relates to a probability of a term showing up in a document. (Note that multiple word terms have a lower probability of occurrence and, therefore, carry a higher weight when they are identified). In one instance, the tagged vocabulary words can be aggregated using streaming databases, where the aggregated tags can be stored and archived in a summarized format.

The resulting information may be suitably categorized in any appropriate format. For example, a dynamic database (e.g., table, list, etc.) can be generated for each individual user, each user-to-user communication (e.g., 1-1, N or N, etc.), and each type of document (e.g., email, phone conversation messages, Meeting Place meeting data, WebEx data, blog posting, White Paper, PDF, Word document, video file, audio file, text message, etc.). Essentially, any type of information propagating in the network can be suitably categorized in the corresponding database of the tendered architecture. Some of the possible database configurations are described below with reference to FIG. 2.

It should be noted that there are two different types of documents flowing through the architecture of communication system 10. Components within communication system 10 can identify which documents should be processed by particular components of the configuration. There is a first set of documents that contain the concept field, which can be used for the vocabulary enhancement activities outlined herein. These documents are anonymous, where the system is indifferent to any association an end user has with these documents. In addition, there is another distinct set of documents that contain whitelisted words/terms exclusively. These documents can be used to build the personal vocabulary, as outlined herein.

In operation of an example that is illustrative, at vocabulary feeder module 44, data can be sent by noun phrase extractor module 64, (i.e., the content field) and this can be used for vocabulary suggestion for administrator 20. This data can be anonymous, having no user concept. For LDAP feeder element 42, whitelisted terms are provided and, further, this can be used for personal vocabulary building, as discussed herein. In essence, this data belongs to a particular user; it is a document associated to a user. Thus, there are two distinct workflows occurring in the architecture, which processes different types of documents for different purposes.

For the business vocabulary workflow, one aspect of the architecture involves a noun phrase extraction component, which can be provided along with filtering mechanisms, and stream access counts to retrieve popular and/or new vocabulary terms. In one example implementation, involving the development of business vocabulary, the architecture can suggest words and phrases that are potential vocabulary candidates. Multi-word phrases can be given more weight than single word terms. The decision whether to include these words in the whitelist or the blacklist can rest with the vocabulary administrator. The administrator can also decide if the words should never be brought to his attention again by marking them for addition to the list of administrator stop words. This can take the form of a feedback loop, for example, from the NCP user interface to the network sensor/central engine (depending on where the stop word removal component may reside).

In one example embodiment, only a certain domain of data (e.g., words) of vocabulary is tagged. As used herein in this Specification, the term ‘data’ is meant to encompass any information (video, text, audio, multimedia, voice, etc.) in any suitable format that propagates in a network environment. The particular domain could be provided in a whitelist, which reflects specific network content. In one example implementation, administrator 20 can develop a certain domain that respects privacy issues, privileged content, etc. such that the ultimate composite of documents or files would reflect information capable of being shared amongst employees in a corporate (potentially public) environment. In certain implementations, the resultant composite of documents (i.e., data) can help to identify experts associated with specific subject matter areas; however, there are a myriad of additional uses to which communication system 10 can apply. As used herein in this Specification, the term ‘resultant composite’ can be any object, location, database, repository, server, file, table, etc. that can offer administrator 20 the results generated by communication system 10.

Turning to the infrastructure of FIG. 1, IP networks 14 and 18 represent a series of points or nodes of interconnected communication paths for receiving and transmitting packets of information, which propagate through communication system 10. IP networks 14 and 18 offer a communicative interface between servers (and/or end users) and may be any local area network (LAN), a wireless LAN (WLAN), a metropolitan area network (MAN), a virtual LAN (VLAN), a virtual private network (VPN), a wide area network (WAN), or any other appropriate architecture or system that facilitates communications in a network environment. IP networks 14 and 18 can implement a TCP/IP communication language protocol in a particular embodiment of the present disclosure; however, IP networks 14 and 18 may alternatively implement any other suitable communication protocol for transmitting and receiving data packets within communication system 10.

Note that central engine 40 can readily be part of a server in certain embodiments of this architecture. In one example implementation, central engine 40 is a network element that facilitates or otherwise helps coordinate the vocabulary building operations, as explained herein. As used herein in this Specification, the term ‘network element’ is meant to encompass network appliances, servers, routers, switches, gateways, bridges, loadbalancers, firewalls, processors, modules, or any other suitable device, component, element, or object operable to exchange information in a network environment. Moreover, the network elements may include any suitable hardware, software, components, modules, interfaces, or objects that facilitate the operations thereof. This may be inclusive of appropriate algorithms and communication protocols that allow for the effective exchange of data or information.

In one example implementation, central engine 40 includes software (e.g., as part of weighting module 55 and/or streaming database feeder 50, etc.) to achieve the personal vocabulary building operations, as outlined herein in this document. In other embodiments, this feature may be provided externally to any of the aforementioned elements, or included in some other network device to achieve this intended functionality. Alternatively, several elements may include software (or reciprocating software) that can coordinate in order to achieve the operations, as outlined herein. In still other embodiments, any of the devices of FIG. 1 may include any suitable algorithms, hardware, software, components, modules, interfaces, or objects that facilitate these vocabulary building operations. Additional operational capabilities of communication system 10 are detailed below with respect to FIGS. 2-3.

Turning to FIG. 2, FIG. 2 is a simplified block diagram of an example implementation of central engine 40. Central engine 40 includes a memory element 86 and a processor 88 in this particular configuration. Central engine 40 also includes a junk filter mechanism 47 (which may be tasked with removing erroneous vocabulary items), a vocabulary module 49, a weighting module 55, a streaming database feeder 50, a MQC 59, a CQC 61, a topics database 63, a collaboration database 65, an indexer module 67, and an index database 69. Indexer module 67 is configured to assist in categorizing the words (and/or noun phrases) collected in communication system 10. Those indices can be stored in index database 69, which can be searched by a given administrator or end user. Along similar reasoning, topics database 63 can store words associated with particular topics identified within the personal vocabulary. Collaboration database 65 can store results provided by multiple end users (e.g., along with administrator 20) in formulating or refining the aggregated personal vocabulary words and/or noun phrases. In regards to vocabulary module 49, this storage area can store the resultant composite of vocabulary words (e.g., per individual), or such information can be stored in any of the other databases depicted in FIG. 2. It is imperative to note that this example of FIG. 2 is merely representing one of many possible configurations that central engine 40 could have. Other permutations are clearly within the broad scope of the tendered disclosure.

In operation of a simplified example used for discussion purposes, the extraction and processing operations can be performed on network sensor 54, where those results may be provided to central engine 40 for building personal vocabulary. With respect to the initial text stripping operations, noun phrase extractor module 64 can find the noun phrases in any text field. In more specific implementations, pronouns and single words are excluded from being noun phrases. A noun phrase can be part of a sentence that refers to a person, a place, or a thing. In most sentences, the subject and the object (if there is one) are noun phrases. Minimally, a noun phrase can consist of a noun (e.g., “water” or “pets”) or a pronoun (e.g., “we” or “you”). Longer noun phrases can also contain determiners (e.g., “every dog”), adjectives (e.g., “green apples”) or other preceding, adjectival nouns (e.g., “computer monitor repair manual”), and other kinds of words, as well. They are called noun phrases because the head word (i.e., the word that the rest of the phrase, if any, modifies) is a noun or a pronoun. For search and other language applications, noun phrase extraction is useful because much of the interesting information in text is carried by noun phrases. Also, most search queries are noun phrases. Thus, knowing the location of the noun phrases within documents and, further, extracting them can be an important step for tagging applications.

For the end-user interface, periodically, terms can be suggested to the administrator for adding to the vocabulary. The existing interface for user-suggested vocabulary could be used for displaying the terms to the administrator. In one example implementation, a stop word removal feature can be provided on central engine 40 (e.g., this could make implementation of the feedback loop more efficient). In other instances, the stop word removal feature is placed on network sensor 54 so that only the filtered fields are sent over to central engine 40. The concept field can be accessible like other fields in the received/collected documents. The concept field is a list of string field values. Additional functionalities associated with these operations are best understood in the context of several examples provided below.

While this is occurring, in a separate workflow personal vocabulary can be developed. Thus, communication system 10 can generate personal vocabulary using corporate vocabulary, which is propagating in the network. In practical terms, it is difficult to tag all user traffic in a corporate (i.e., enterprise) environment. There are two modes in which corporate vocabulary can be generated. First, in a learning mode, where end users are not yet subscribed, automatic corporate vocabulary can be generated by tagging content as it flows through the network. This can be generated by tagging content anonymously in the network. This typically happens in the learning mode of the system, where no users are subscribed on the system. The user whose content is being tagged is not necessarily of interest at the time of corporate vocabulary generation. Second, in a real-time system scenario, as users begin using the system, users have the ability to suggest new words to the corporate vocabulary through a manual process, feedback loops, etc., which are detailed herein.

By contrast, personal vocabulary generation can use corporate vocabulary to tag words for particular users. As documents (e.g., email/http/videos, PDF, etc.) flow through the network, the system checks for words from the corporate vocabulary, tags the appropriate words (e.g., using a whitelist), and then associates those words with particular users. Communication system 10 can include a set of rules and a set of algorithms that decide whether tagged words should be added to a personal vocabulary. Rules include common term threshold, group vocabulary adjustment, etc. Over a period of time, the user's personal vocabulary develops into a viable representation of subject areas (e.g. categories) for this particular end user. In addition, the user has the ability to add words to his personal vocabulary manually. He also has the ability to mark individual words as public or private, where the latter would prohibit other users in the system from viewing those personal vocabulary words.

Many of these activities can be accomplished by using streaming databases in accordance with one example implementation. In one particular instance, this involves the use of streaming database feeder 50. A streaming database continuously analyzes massive volumes of dynamic information. Streaming database feeder 50 can create a user sub-stream for each user, where the tags could continuously be updated for that user. By writing a simple query, an individual can derive the most prevalent topics (e.g., based on a normalized count and time).

FIGS. 3 and 4 offer two distinct workflows for communication system 10. FIG. 3 addresses the corporate vocabulary formation, whereas FIG. 3 addresses the personal vocabulary development. FIG. 3 is a simplified flowchart illustrating one example operation associated with communication system 10. In this particular flow, at step 110, end user 12 has written an email that includes the content “Optical Switching is a terrific technology.” This email message can traverse the network and be received at a router (e.g., a large corporate router, a switch, a switched port analyzer (SPAN) port, or some type of virtual private network (VPN) network appliance). This is reflected by step 120. Network sensor 54 can be provisioned at such a location in order to capture data and/or facilitate the identification of content, as described herein.

In this particular example, FIFO element 56 may receive data in a raw format at step 130. Text extraction module 58 may extract certain fields in order to identify a title, text, authorship, and a uniform resource locator (URL) associated with this particular document at step 140. [Note that as used herein in this Specification, the term ‘separate’ is used to encompass extraction, division, logical splitting, etc. of data segments in a data flow. The term ‘tag’ as used herein in this Specification, is used to encompass any type of labeling, maintaining, identifying, etc. associated with data.] Note that for this particular instance (where an email is being sent), the URL can have a blank field. The title may include a subject line, or an importance/priority parameter, and the text field would have the quoted statement (i.e., content), as written above. The document is then passed to blacklist 60, which searches (i.e., evaluates) the document to see if any blacklisted words are found in the document (step 150). If any such blacklisted words are present, the document is dropped. In one general sense, there are two layers of privacy provided by blacklist 60 and whitelist 66, which are working together. Examples of blacklist words in a corporate environment may include ‘salary’, ‘merger’, etc., or possibly words that might offend public users, compromise privacy issues, implicate confidential business transactions, etc. Note that the blacklist (much like the whitelist) can readily be configured by administrator 20 based on particular user needs. The term ‘whitelist’ as used herein in this Specification is meant to connote any data sought to be targeted for inclusion into the resultant composite of words for administrator 20. Along similar reasoning, the term ‘blacklist’ as used herein is meant to include items that should not be included in the resultant composite of words.

Provided that the document in this instance is not dropped as a result of the blacklist check, the document passes to document filter 62. Document filter 62 performs a quick check of the type of document that is being evaluated at step 160. Again, this component is configurable as an administrator can readily identify certain types of documents as including more substantive or meaningful information (e.g., PDF or Word processing documents, etc.). Along similar reasoning, some documents (such as JPEG pictures) may not offer a likelihood of finding substantive vocabulary (i.e., content) within the associated document. These more irrelevant documents may be (as a matter of practice) not evaluated for content and any such decision as to whether to ignore these documents (e.g., JPEG pictures), or scrutinize them more carefully would be left up to administrator 20.

In one example, noun phrase extractor module 64 includes a natural language processing (NLP) component to assist it in its operations. Note that a similar technology may exist in text extraction module 58 to assist it in its respective operations. One objective of noun phrase extractor module 64 is to extract meaningful objects from within text such that the content can be aggregated and further processed by communication system 10. In this example, noun phrase extractor module 64 performs its job by extracting the terms “optical switching” and “technology.” This is illustrated by step 170.

Once this document has propagated through noun phrase extractor module 64, the document passes to whitelist 66 at step 180. An administrator may wish to pick up certain whitelisted words in the content, as it propagates through a network. The whitelist can be used on various fields within communication system 10. In this particular example, the whitelist is used to search the title and text fields. At this point, the document is sent to document splitter element 68. Note that there are two documents being created from the original document. In one instance, document splitter element 68 can receive a document with five fields including the concept field (at step 190), and perform several operations. First, it creates document #2 using the concept field in document #1. Second, it removes the concept field from document #1. Third, it can remove all fields except the concept field from document #2. Fourth, it can send both document #1 and document #2 to clean topics module 70.

It should be noted that noun phrase extractor module 64 operates best when considering formal statements (e.g., using proper English). Colloquialisms or folksy speech is difficult to interpret from the perspective of any computer system. More informal documentation (e.g., email) can be more problematic, because of the speech that dominates this forum.

Clean topics module 70 is configured to address some of these speech/grammar issues in several ways. In one example implementation, clean topics module 70 can receive two documents, as explained above. It passes document #1 without the concept field. For document #2, having the concept field, it can be configured to employ stop word removal logic at step 200. In this particular arrangement, the following stop words can be removed: first name, last name, userid; functional stop word: A, an, the, etc.; email stop words: regards, thanks, dear, hi, etc.; non-alphabets: special characters, numbers; whitelist words: all words found in a whitelist file configured by the administrator; administrator stop words: administrator rejected system words. Note that the operation of filtering functional stop words is different from filtering email (e.g., administrator stop words). For example, “Back Of America” would not be processed into “Bank America.” Thus, stop words between two non-stop words would not necessarily be removed in certain instances.

In addition, and in this particular example, the following rules can be applied: Rule 1: Remove the entire noun phrase if a substring match is found; Rule 2: Remove only the offending culprit; Rule 3: Remove the entire noun phrase if an exact match is found. Particular to this example, rules can be applied in the following order: Drop concept fields containing non-alphabets (Rule 1); Drop concept fields containing (e.g., LDAP) entries (Rule 1); Drop concept fields containing email stop words (Rule 1); Remove the functional stop word only if it is at either end of the concept field. Do not drop the words found in between, apply rule iteratively (Rule 2). Drop the concept field value if it is an exact match with the whitelist words (Rule 1). Drop the concept field value if it is an exact match with the administrator stop words (Rule 1). Note that LDAP filtering can also occur during these activities. For example, if any proper names already in LDAP are identified, the filter can just drop those terms.

Vocabulary feeder module 44 can receive the documents (e.g., on the central engine side) at step 210. Vocabulary feeder module 44 forwards the document without the concept field and, for the document with the concept field, it sends it to streaming database feeder 50. In one instance, the streams are associated with storage technology, which is based on a stream protocol (in contrast to a table format). In other instances, any other suitable technology can be employed to organize or to help process the incoming documents, content, etc. The streams can be updated by vocabulary feeder module 44.

More specifically, the analytics approach of central engine 40 (in one example) involves having queries analyze streaming data. This strategy for handling continuously flowing data is different from traditional business intelligence approaches of first accumulating data and then running batch queries for reporting and analysis. Such an approach enables analysis of heterogeneous data regardless of whether the data is flowing, staged, etc. In addition, queries are continuous and constantly running so new results are delivered when the downstream application can use them. Data does not need to be stored or modified, so the system can keep up with enormous data volumes. Thousands of concurrent queries can be run continuously and simultaneously on a server architecture. Queries can be run over both real-time and historical data. Incoming data can be optionally persisted for replay, back-testing, drill-down, benchmarking, etc.

Returning to the flow of FIG. 3, vocabulary feeder module 44 can read the concept field (e.g., created by the NLP module) and can feed the noun phrases to the raw vocabulary stream (e.g., “raw_vocab_stream” file) at step 220. The vocabulary feeder mechanism can calculate the weight of each of the topics in the concept field by looking up a hash map (initialized from a file) between the number of terms and corresponding weight and, subsequently, feed the topic, calculated weight, and timestamp into the raw vocabulary stream. The vocabulary feeder's output can be configured to interface with the vocabulary stream. The streams aggregate the topics into (for example) a weekly collapsed vocabulary table (e.g., “weekly_collapsed_vocab_table” file), which could be updated during any suitable timeframe (e.g., hourly). This table serves as input to table write service element 48.

In regards to the periodic write service, a periodic service can invoke the write to administrator table service, as explained above. This service can be configurable for the following: silent mode, hourly, daily, weekly, monthly. Hourly, daily, weekly, and monthly modes designate that the terms are suggested to an administrator on the specified intervals. Hourly intervals could be used for testing purposes. A silent mode offers a file based approach, where terms are written to a file, and do not make it to the administrator user interface.

For table write service element 48, a service layer can read the weekly collapsed vocabulary table for the top words and write to the administrator user interface table. The administrator user interface table can represent the shared table between user-suggested vocabulary terms and the system suggested vocabulary terms. Administrator suggest interface 38 can read the user-suggested vocabulary table (“userSuggestedVocabulary table”) to display the terms. This module can suggest the top ‘n’ words to the administrator for adding to the vocabulary whitelist. Feedback loop module 36 may include application program interfaces (APIs) being provided to create a file from the table of suggested vocabulary terms.

In this example, administrator suggest interface 38 reads the weekly collapsed vocabulary table to display the terms at step 230. This element also suggests the top (e.g., ‘n’) words to administrator 20 for addition to the vocabulary whitelist. The administrator is provided a user interface to make decisions as to whether to add the term to the whitelist, add it to the blacklist, or to ignore the terms. In one example implementation, the administrator does not suggest new stop words. Only system suggested (or user suggested) stop words can be rejected.

Feedback loop module 36 is coupled to administrator suggest interface 38. In case the administrator chooses the “reject term” option, the system can add the term to the list of existing stop words and, further, propagate it to network sensor 54 to copy over to a file (e.g., adminStopWords.txt). This is reflected by step 240. Network collaboration platform 32 can create a file from the table suggested vocabulary terms (e.g., via commands including suggestedby=system, and status=rejected). This file can be a part of the force sync files that can be pushed to the network sensor/central engine (depending on where the stop words mechanism resides). At step 260, emerging vocabulary topics element 46 can look up emerging topics (e.g., within harvested documents) and, systematically, add the emerging and top topics to the architecture for the administrator to consider. Both options can be provided to administrator 20. The emerging topics can be similar to the experience tags such that topics growing in prominence over a given time interval (e.g., a week) can be suggested to administrator 20.

FIG. 4 is a simplified flowchart illustrating one example operation associated with communication system 10. In this particular flow, an email is written from a first end user (John) to a second end user (Bill) at step 210. The email from John states, “Search engines are good” and this is evaluated in the following ways. First, authorship is identified and the email is searched for blacklisted and whitelisted words at step 220. In essence, a number of text stripping operations occur for the received document (as outlined previously above in FIG. 3). Second, the whitelisted words are received at LDAP feeder element 42 at step 230. In one sense, the appropriate concept has been extracted from this email, where insignificant words have been effectively stripped from the message and are not considered further.

At step 240, John is associated with the term “search engine” based on John authoring message and, in a similar fashion, Bill is associated with the term “search engine” based on him receiving this message. Note that there is a different weight associated with John authoring this message, and Bill simply receiving it. At step 250, weighting module 55 can be invoked in order to assign an intelligent weight based on this message propagating in the network. For example, as the author, John may receive a full point of weight associated with this particular subject matter (i.e., search engines). As the recipient, Bill may only receive a half point for this particular subject matter relationship (where Bill's personal vocabulary would include this term, but it would not carry the same weight as this term being provided in John's personal vocabulary).

In addition, and as reflected by step 260, weighting module 55 may determine how common this word choice (i.e., “search engine”) is for these particular end users. For example, if this is the first time that John has written of search engines, it would be inappropriate to necessarily tag this information and, subsequently, identify John as an expert in the area of search engines. This email could be random, arbitrary, a mistake, or simply a rare occurrence. However, if over a period of time, this terminology relating to search engines becomes more prominent (e.g., reaching a threshold), then John's personal vocabulary may be populated with this term.

In this particular example, several days after the initial email, John sends Bill a second email that includes a white paper associated with search engines, along with an accompanying video that is similarly titled. This is reflected by step 270. Central engine 40 has the intelligence to understand that a higher weight should be accorded to this subsequent transmission. Intuitively, the system can understand that certain formats (White Papers, video presentations, etc.) are more meaningful in terms of associating captured words with particular subject areas. At step 280, weighting module 55 assigns this particular transmission five points (three points for the White Paper and two points for the video presentation), where the five points would be allocated to John's personal vocabulary associated with search engines. In addition, Bill is also implicated by this exchange, where he would receive a lesser point total for (passively) receiving this information. In this instance, and at step 290, Bill receives three points as being a recipient on this email. At step 300, the point totals are stored in an appropriate database on a per-user basis.

Additionally, over time, a social graph can be built based on the connection between John and Bill and, in particular, in the context of the subject area of search engines. In one sense, the weight between these two individuals can be bidirectional. A heavier weight is accorded to John based on these transmissions because he has been the dominant author in these exchanges. If Bill were to become more active and assume an authorship role in this relationship, then the weight metric could shift to reflect his more proactive involvement. In one particular example, a threshold of points is reached in order for Bill's personal vocabulary to include the term ‘search engine.’ This accounts for the scenario in which a bystander is simply receiving communications in a passive manner.

The architecture discussed herein can continue to amass and aggregate these counts or points in order to build a personal vocabulary (e.g., personal tags) for each individual end user. The personal vocabulary is intelligently partitioned such that each individual has his own group of tagged words to which he is associated. At the same time, a social graph can continue to evolve as end users interact with each other about certain subject areas.

In contrast to other systems that merely identify two individuals having some type of relationship, the architecture provided herein can offer the context in which the relationship has occurred, along with a weighting that is associated with the relationship. For example, with respect to the John/Bill relationship identified above, these two individuals may have their communications exclusively based on the topic of search engines. Bill could evaluate his own personal vocabulary and see that John represents his logical connection to this particular subject matter. He could also evaluate other less relevant connections between his colleagues having (in this particular example) a weaker relationship associated with this particular subject matter. Additionally, an administrator (or an end user) can construct specific communities associated with individual subject matter areas. In one example, an administrator may see that John and Bill are actively involved in the area of search engines. Several other end users can also be identified such that the administrator can form a small community that can effectively interact about issues in this subject area.

In another example, entire groups can be evaluated in order to identify common subject matter areas. For example, one group of end users may be part of a particular business segment of a corporate entity. This first group may be associated with switching technologies, whereas a second group within the corporate entity may be part of a second business segment involving traffic management. By evaluating the vocabulary exchanged between these two groups, a common area of interest can be identified. In this particular example, the personal vocabulary being exchanged between the groups reveals a common interest in the subject of deep packet inspection.

Note that one use of the resulting data is to create a dynamic file for each individual user that is tracked, or otherwise identified through communication system 10. Other applications can involve identifying certain experts (or group of experts) in a given area. Other uses could involve building categories or subject matter areas for a given corporate entity. Note also that communication system 10 could accomplish the applications outlined herein in real time. Further, the association of the end users to particular subject matter areas can then be sent to networking sites, which could maintain individual profiles for a given group of end users. This could involve platforms such as Facebook, LinkedIn, etc. The dynamic profile can be supported by the content identification operations associated with the tendered architecture. In other applications, video, audio, and various multimedia files can be tagged by communication system 10 and associated with particular subject areas, or specific end user groups. In one instance, both the end user and the video file (or the audio file) can be identified and logically bound together or linked.

Software for providing intelligent vocabulary building can be provided at various locations. In one example implementation, this software is resident in a network element, such as central engine 40 and/or network sensor 54, or in another network element for which this capability is relegated. In other examples, this could involve combining central engine 40 and/or network sensor 54 with an application server or a gateway, or some proprietary element, which could be provided in (or be proximate to) these identified network elements, or this could be provided in any other device being used in a given network. In one specific instance, central engine 40 provides the personal vocabulary building features explained herein, while network sensor 54 can be configured to offer the business vocabulary activities detailed herein. In such an implementation, network sensor 54 can initially receive the data, employ whitelist filtering, and then send those selected nouns and noun phrases to central engine 40 to develop or otherwise process personal vocabulary.

In other embodiments, the vocabulary building feature may be provided externally to network sensor 54, or included in some other network device, or in a computer to achieve these intended functionalities. As identified previously, a network element can include software to achieve the vocabulary building operations, as outlined herein in this document. In certain example implementations, the vocabulary building functions outlined herein may be implemented by logic encoded in one or more tangible media (e.g., embedded logic provided in an application specific integrated circuit [ASIC], digital signal processor [DSP] instructions, software [potentially inclusive of object code and source code] to be executed by a processor, or other similar machine, etc.). In some of these instances, a memory element [as shown in FIG. 2] can store data used for the operations described herein. This includes the memory element being able to store software, logic, code, or processor instructions that are executed to carry out the activities described in this Specification. A processor can execute any type of instructions associated with the data to achieve the operations detailed herein in this Specification. In one example, the processor [as shown in FIG. 2] could transform an element or an article (e.g., data) from one state or thing to another state or thing. In another example, the activities outlined herein may be implemented with fixed logic or programmable logic (e.g., software/computer instructions executed by a processor) and the elements identified herein could be some type of a programmable processor, programmable digital logic (e.g., a field programmable gate array [FPGA], an erasable programmable read only memory (EPROM), an electrically erasable programmable ROM (EEPROM)) or an ASIC that includes digital logic, software, code, electronic instructions, or any suitable combination thereof.

Any of these elements (e.g., the network elements, etc.) can include memory elements for storing information to be used in achieving the vocabulary building operations as outlined herein. Additionally, each of these devices may include a processor that can execute software or an algorithm to perform the vocabulary building activities as discussed in this Specification. These devices may further keep information in any suitable memory element [random access memory (RAM), ROM, EPROM, EEPROM, ASIC, etc.], software, hardware, or in any other suitable component, device, element, or object where appropriate and based on particular needs. Any of the memory items discussed herein should be construed as being encompassed within the broad term ‘memory element.’ Similarly, any of the potential processing elements, modules, and machines described in this Specification should be construed as being encompassed within the broad term ‘processor.’ Each of the network elements can also include suitable interfaces for receiving, transmitting, and/or otherwise communicating data or information in a network environment.

Note that with the examples provided herein, interaction may be described in terms of two, three, four, or more network elements. However, this has been done for purposes of clarity and example only. In certain cases, it may be easier to describe one or more of the functionalities of a given set of flows by only referencing a limited number of components or network elements. It should be appreciated that communication system 10 of FIG. 1 (and its teachings) are readily scalable. Communication system 10 can accommodate a large number of components, as well as more complicated or sophisticated arrangements and configurations. Accordingly, the examples provided should not limit the scope or inhibit the broad teachings of communication system 10 as potentially applied to a myriad of other architectures.

It is also important to note that the steps described with reference to the preceding FIGURES illustrate only some of the possible scenarios that may be executed by, or within, communication system 10. Some of these steps may be deleted or removed where appropriate, or these steps may be modified or changed considerably without departing from the scope of the discussed concepts. In addition, a number of these operations have been described as being executed concurrently with, or in parallel to, one or more additional operations. However, the timing of these operations may be altered considerably. The preceding operational flows have been offered for purposes of example and discussion. Substantial flexibility is provided by communication system 10 in that any suitable arrangements, chronologies, configurations, and timing mechanisms may be provided without departing from the teachings of the discussed concepts. 

What is claimed is:
 1. A method, comprising: receiving data propagating in a network environment at a streaming database feeder; ignoring Joint Photographic Experts Group (JPEG) documents in the data; updating tags for each user in the network environment using a user-sub stream created for the user by the streaming database feeder, wherein each user-sub stream includes at least a portion of the data propagating in the network environment, wherein the tags are words and phrases that are associated with each user, wherein the data includes documents and, for at least a portion of the documents in the data, each original document is copied to create an anonymous document and a document that contains selected words within the data based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged, wherein documents that include data in a blacklist are dropped, and wherein the anonymous documents contain a concept field and some of the data in the anonymous documents is selected for the whitelist, and wherein the document that contains selected words does not include the concept field; assigning a weight to the selected words based on at least one characteristic associated with the data; associating the selected words to an individual, wherein the weight for a selected word is higher if the individual propagates the data; and generating a resultant composite of the selected words that are tagged.
 2. The method of claim 1, further comprising: partitioning the resultant composite amongst a plurality of individuals associated with the data propagating in the network environment; and generating a social graph that identifies a relationship between a selected individual and the plurality of individuals based on a plurality of words exchanged between the selected individual and the plurality of individuals.
 3. The method of claim 1, further comprising: determining whether a threshold weight value associated with the selected words has been met, wherein if the threshold has been met, then the selected words are included in the resultant composite.
 4. The method of claim 1, further comprising: determining whether the data is sent or received by the individual, wherein a different weight value is assigned to the selected words based on the selected words being sent or received by the individual.
 5. The method of claim 1, further comprising: determining whether the selected words have been marked as private, wherein if the selected words are marked as private, then the selected words are not included in the resultant composite.
 6. The method of claim 1, wherein the characteristic relates to a probability of the selected words appearing in a document that includes the data, and wherein individual words that have a lower probability of appearing in the document are assigned a higher weight value.
 7. The method of claim 1, wherein the characteristic relates to a document type that includes the data.
 8. The method of claim 1, wherein a plurality of anonymous documents are used to create a corporate vocabulary and a plurality of documents that contains selected words are used to create a personal vocabulary.
 9. The method of claim 1, wherein the JPEG documents are excluded from the data.
 10. Logic encoded in one or more non-transitory media that includes code for execution and when executed by a processor is operable to perform operations comprising: receiving data propagating in a network environment at a streaming database feeder; ignoring Joint Photographic Experts Group (JPEG) documents from the data; updating tags for each user in the network environment using a user-sub stream created for the user by the streaming database feeder, wherein each user-sub stream includes at least a portion of the data propagating in the network environment, wherein the tags are words and phrases that are associated with each user, wherein the data includes documents and, for at least a portion of the documents in the data, each original document is copied to create an anonymous document and a document that contains selected words within the data based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged, wherein documents that include data in a blacklist are dropped, and wherein the anonymous documents contain a concept field and some of the data in the anonymous documents is selected for the whitelist, and wherein the document that contains selected words does not include the concept field; assigning a weight to the selected words based on at least one characteristic associated with the data; associating the selected words to an individual, wherein the weight for a selected word is higher if the individual propagates the data; and generating a resultant composite of the selected words that are tagged.
 11. The logic of claim 10, the processor being further operable to perform operations comprising: partitioning the resultant composite amongst a plurality of individuals associated with the data propagating in the network environment; and generating a social graph that identifies a relationship between a selected individual and the plurality of individuals based on a plurality of words exchanged between the selected individual and the plurality of individuals.
 12. The logic of claim 10, the processor being further operable to perform operations comprising: determining whether a threshold weight value associated with the selected words has been met, wherein if the threshold has been met, then the selected words are included in the resultant composite.
 13. The logic of claim 10, the processor being further operable to perform operations comprising: determining whether the data is sent or received by the individual, wherein a different weight value is assigned to the selected words based on the selected words being sent or received by the individual.
 14. The logic of claim 10, the processor being further operable to perform operations comprising: determining whether the selected words have been marked as private, wherein if the selected words are marked as private, then the selected words are not included in the resultant composite.
 15. The logic of claim 10, wherein the characteristic relates to a probability of the selected words appearing in a document that includes the data, and wherein individual words that have a lower probability of appearing in the document are assigned a higher weight value.
 16. The logic of claim 10, wherein the characteristic relates to a document type that includes the data.
 17. An apparatus, comprising: a memory element configured to store data; a processor operable to execute instructions associated with the data; a network sensor configured to interface with the memory element and the processor, the network sensor being configured to: receive data propagating in a network environment at a streaming database feeder; ignore Joint Photographic Experts Group (JPEG) documents from the data; update tags for each user in the network environment using a user-sub stream created for the user by the streaming database feeder, wherein each user-sub stream includes at least a portion of the data propagating in the network environment, wherein the tags are words and phrases that are associated with each user, wherein the data includes documents and, for at least a portion of the documents in the data, each original document is copied to create an anonymous document and a document that contains selected words within the data based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged, wherein documents that include data in a blacklist are dropped, and wherein the anonymous documents contain a concept field and some of the data in the anonymous documents is selected for the whitelist, and wherein the document that contains selected words does not include the concept field; and a weighting module configured to: assign a weight to the selected words based on at least one characteristic associated with the data, wherein the selected words are associated to an individual and the weight for a selected word is higher if the individual propagates the data, and wherein a resultant composite of the selected words that are tagged is generated.
 18. The apparatus of claim 17, further comprising: a database configured to partition the resultant composite amongst a plurality of individuals associated with the data propagating in the network environment, and wherein the database includes a social graph that identifies a relationship between a selected individual and the plurality of individuals based on a plurality of words exchanged between the selected individual and the plurality of individuals.
 19. The apparatus of claim 17, wherein the weighting module is further configured to determine whether a threshold weight value associated with the selected words has been met, wherein if the threshold has been met, then the selected words are included in the resultant composite.
 20. The apparatus of claim 17, wherein a different weight value is assigned to the selected words based on the selected words being sent or received by the individual. 